In [24]:
import pandas as pd
import seaborn as sns
import plotly.express as px

import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
In [2]:
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib¶

For this excercise, we have written the following code to load the stock dataset built into plotly express.

In [20]:
stocks = px.data.stocks()
stocks.head(100)
Out[20]:
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708
... ... ... ... ... ... ... ...
95 2019-10-28 1.155603 1.461829 1.457474 1.036232 1.365827 1.629663
96 2019-11-04 1.189743 1.486514 1.452951 1.021354 1.388495 1.655063
97 2019-11-11 1.211063 1.518629 1.415209 1.044153 1.404972 1.700533
98 2019-11-18 1.175199 1.495886 1.420278 1.064062 1.478547 1.696224
99 2019-11-25 1.183927 1.527143 1.465089 1.079154 1.498452 1.716521

100 rows × 7 columns

Question 1:¶

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

In [34]:
# YOUR CODE HERE
plot = stocks.plot(x='date', y='GOOG')
plot.set_title('Google Stock')
plot.set_ylabel('Stock Value')
plt.rcParams["figure.figsize"] = (15,30)

plt.show()


# Towards point 0-98

Question 2:¶

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

In [43]:
plot = stocks.plot(x='date', y=['GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT'])
plot.set_title('Google Stock')
plot.set_ylabel('Stock Value')
plt.rcParams["figure.figsize"] = (15,10)

plt.show()

Seaborn¶

First, load the tips dataset

In [119]:
tips = sns.load_dataset('tips')
tips.head()
Out[119]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

Question 3:¶

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

  • Are there differences between male and female when it comes to giving tips?
  • What attribute correlate the most with tip?
In [125]:
# YOUR CODE HERE
print("Question: What are the correlations between total bill and tips, sorted by day and gender?")


splot = sns.FacetGrid(tips, col='day', hue='sex')
splot.map(sns.scatterplot, 'total_bill', 'tip')
splot.add_legend()

plt.show()

Question: What are the correlations between total bill and tips, sorted by day and gender?

Plotly Express¶

Question 4:¶

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset¶

Hints:

  • Turn stocks dataframe into a structure that can be picked up easily with plotly express
In [52]:
df = px.data.stocks()
fig = px.line(df, x='date', y=['GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT'])
fig.show()

The tips dataset¶

In [127]:
# YOUR CODE HERE
df = px.data.tips()
fig = px.scatter(df, x='total_bill', y='tip', color='sex', facet_col='day')
fig.show()

Question 5:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [69]:
#load data
df = px.data.gapminder()
df.head()
Out[69]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4
In [116]:
# year_2007 = df['year'] == 2007
# df[year_2007]

year_2007_bar = px.data.gapminder().query('year == 2007')
fig = px.bar(year_2007_bar, x='continent', y='pop', color='continent', text = 'country')

fig.update_xaxes(categoryorder = "total descending")

fig.show()
In [ ]: